Instead of falling for the latest claim of a simple ‘quick-fix’ solution, organisations like Google have embraced complexity by placing networks at the heart of what they do. Networks of data, computers and artificial neurons can model complex systems. Any organisation wishing to future-proof itself and remain competitive must adopt this ‘networked model’ without delay. (View Highlight)
Organisations wishing to successfully transition to the Information Age must learn to spot the lines in their businesses and, where necessary, be willing to move beyond them. In general, our current infrastructures are not well placed for this task and it is not just because individually the applications and databases are too rigid and inflexible. The biggest problem lies in the complicated and discordant mess that has been created by trying to connect these individual parts into a single entity. (View Highlight)
We need a new coordinated toolset that allows us to look beyond the lines that divide our organisations’ information into separate silos through to the less overt but far more illuminating curves and circles which connect it into one unified system. A toolset that cuts across information boundaries and takes a systemic view is one that allows us to embrace our organisations’ inherent and enriching complexity. (View Highlight)
Furthermore, there is another dimension to complexity. Information is not just connected, it is also constantly changing and as one thing changes it often has a knock-on effect on the things to which it is connected. It is worth acknowledging that we tend to think about cause and effect linearly. In other words, we tend to think one thing affects another that in turn then affects another. Like a line of dominoes, you knock over the first one and the cascade of cause and effects ripples down the line. A causes B which causes C. (View Highlight)
Again the reality is more subtle and complex than this. Causality too can contain circles. A causes B which causes C which causes A. These circles in time are known as feedback loops. If you imagine a couple having a conversation where one partner raises their voice slightly which causes the other partner to raise their voice which causes the first partner to raise theirs higher and, before you know it, the couple are having a full-blown row, plates are flying and you have the full catastrophe. Hurricanes, swarms of bees, and flash mob parties are all manifestations of non-linear bi-directional causality. (View Highlight)
The second law of thermodynamics states that in an isolated system the disorder tends to increase with time. Things tend to get messier (anyone with young children will attest to this) which gives us an arrow showing the direction in which time is flowing - time is flowing towards a great big mess. (View Highlight)
However, feedback loops (like Maxwell’s demon) allow information to be recorded. This is important as now we have the physical system plus information. Complex layered networks of feedback loops (like plants, animals, and yourself) can learn from the past so that predictions about the future affect actions in the present. This seemingly reverses the ‘arrow of time’ and entropy goes backwards; order increases within the isolated system. In a universe where everything gets messier, plants, language, and robotic vacuum cleaners swim against the tide, it is as if the mess is self-organising and cleaning itself up. (View Highlight)
Balancing feedback loops keep a system as it is. Think of the thermostat in your house that turns the hot water off or on based upon how close the current temperature is to the desired target temperature. Blood sugar regulation, supply and demand and the carbon cycle are all examples of balancing feedback loops. Balancing feedback loops are key to maintaining sustainable systems. (View Highlight)
Reinforcing feedback loops, on the other hand, are like engines of change, they cause both growth and decay within the system. Bacterial growth, compound interest and the said couple’s escalating argument are all examples of reinforcing feedback loops. Reinforcing feedback loops are key to powering growth. Left unchecked reinforcing feedback loops can lead to exponential change, which to our linear thinking brains can sometimes look like a line that goes from horizontal to vertical in a split second. (View Highlight)
All organisations are both held together and propelled forward by feedback loops, as are the wider markets within which they operate. A complexity embracing mindset allows us to see these feedback loops and to appreciate their deep anti-entropic (i.e. self-organising) power. Organisations now need a new supporting toolset that can model these feedback loops, harnessing their power to maximise new opportunities and to avert internal decay. (View Highlight)
We can also use feedback loops to understand changes in society and modern organisations need to recognise a fundamental reinforcing feedback loop that exists in human society right now: that is the one between complexity and the rate of change. (View Highlight)
Our world is becoming ever more networked and technologically complex, increasing the rate at which things change, which in turn means that technology gets more complex more quickly, which in turn … well, I think you get the idea. (View Highlight)
Balancing feedback loops (like that thermostat in your house) create a stable platform on which we can ‘conduct business as usual’; but sometimes a powerful reinforcing feedback loop (like the complexity-change one) will overpower the balancing loops that keep the system in equilibrium. In other words, the system quickly flips into a new normal. A good example of this is that water as it gets colder and colder and suddenly turns into a block of ice, or a caterpillar as it transforms into a butterfly. These sudden changes are called phase transitions. (View Highlight)
To begin to answer that let’s return to lines and curves. A defining feature of industrial society is that it is based on straight lines. If industry was a shape, it would be a rectangular box. We could say that ‘industrial thinking’ is ‘box-shaped thinking’. This linear thinking is reflected in the environments we have created, and the way we view and interact with the world. The evidence for this is all around us and right before our eyes. (View Highlight)
Since the Enlightenment, we have used science, mechanics and analysis to take things apart and to understand the intricacies of each part in isolation. You have much greater control if you ignore the connections and just examine a single part in sealed laboratory conditions. You can develop precise linear equations where known inputs produce known outputs and describe how everything works very accurately and reliably. Analysis is an extremely powerful tool and has allowed us to build the highly effective machines and science of the industrial age. It is the fundamental foundation upon which we now stand and it will necessarily remain crucial to our progress as we move forward. (View Highlight)
three main forces driving the technological phase transition are:
DATA: data that contains greater variety, arriving in increasing volume and with more velocity
CLOUD: networked computing facilities providing remote data storage and processing services via the internetAI: computer systems able to perform tasks normally requiring human intelligence
These three elements are deeply interconnected, and the combined power of their feedback loop is probably best expressed on the internet and manifest in the giant internet platforms that are coming to ascendance. (View Highlight)
Many organisations look at Amazon, Google and Facebook from afar and just focus on the new and exciting AI that these companies are producing, but the reason AI works so well for them is that they have invested in and spent time amassing, huge amounts of data from the web — and AI needs lots of data. AI is like the tip of the data iceberg because behind every cleaver algorithm is a mass of highly processed data. Unfortunately, becoming a data-rich internet platform is not a realistic option for many organisations. What most organisations do have, however, is all the internal data that they have carefully collected and curated over the last 10 to 100 years. Therefore, the name of the game is to connect all this information together; to combine the individual parts into a single whole whilst leaving the data where it is. The result would amount to more than enough data to train your AI on, and the models will be highly relevant to your organisation’s niche and mission. For a bank this might be fraud detection, for a hospital disease recognition, for a manufacturer supply chain optimisation and so on. It is impossible to even anticipate all the potential applications, as the connected summation of an organisation’s data should be far greater than its parts. (View Highlight)
The problem is that organisational data is currently fractured, fragmented and hidden away in a multitude of different systems, making data integration a nightmare. The data-lakes are overwhelmed by the complexity, and they are starting to turn into swamps, starving the AI of the data on which it depends (View Highlight)
We can embrace complexity by examining the connections between the parts, and that is exactly what networks allow us to do. The outrageous yet exciting possibility is that we can combine all three separate networks (data, cloud and AI) into a single unified network. This changes everything because now all organisations have the potential to build their own internal networks and to begin embracing their enriching complexity. (View Highlight)
The internet giants are well ahead in this game but the game has only just begun and now some other organisations, for example, some governments, investment banks, retailers and pharmaceuticals are beginning to engage with network-ification too. This article is an attempt to clarify this effort and to bring it together by showing how we can use the network-shape as our guiding north star. More pragmatically, we can create a network-ification toolset that will allow all organisations to think outside the box, and the good news is, that the next parts of this article will give you a broad overview of exactly how this can be done. (View Highlight)
